def _test(): """Tests""" import ClearMap.Visualization.Plot3d as p3d import ClearMap.ImageProcessing.IlluminationCorrection as ic ff = ic.flatfield_from_line(ic.default_flat_field_line_file_name, 100); p3d.plot(ff)
def _test():
import numpy as np
import ClearMap.Test.Files as tst
import ClearMap.Visualization.Plot3d as p3d
import ClearMap.ImageProcessing.Differentiation as dif
from importlib import reload
reload(dif);
source = tst.init('v')[:20, :20, :20];
sink = dif.eigenvalues(source, sigma = 1.0);
p3d.plot([source] + list(sink.transpose([3,0,1,2])));
sink = np.zeros(source.shape + (3,), order = 'C');
dif.eigenvalues(source, sink, sigma = 1.0);
p3d.plot([source] + list(sink.transpose([3,0,1,2])));
sink = np.zeros(source.shape + (3,), order = 'F');
dif.eigenvalues(source, sink, sigma = 1.0);
p3d.plot([source] + list(sink.transpose([3,0,1,2])));
sink = np.zeros(source.shape);
dif.lambda123(source, sink, sigma = 1.0);
p3d.plot([source, sink])
sink = dif.lambda123(source, sigma = 1.0, threshold = 0.2);
p3d.plot([source, sink]);
def _test():
"""Tests"""
import ClearMap.Tests.Files as tsf
import ClearMap.Visualization.Plot3d as p3d
import ClearMap.ImageProcessing.LightsheetCorrection as ls
from importlib import reload
reload(ls)
s = tsf.source('vasculature_lightsheet_raw')
#p3d.plot(s)
import ClearMap.ImageProcessing.Experts.Vasculature as vasc
clipped, mask, high, low = vasc.clip(s[:, :, 80:120],
clip_range=(400, 60000))
corrected = ls.correct_lightsheet(clipped,
mask=mask,
percentile=0.25,
lightsheet=dict(selem=(150, 1, 1)),
background=dict(selem=(200, 200, 1),
spacing=(25, 25, 1),
step=(2, 2, 1),
interpolate=1),
lightsheet_vs_background=2)
p3d.plot([clipped, corrected])
def _test():
"""Tests."""
import numpy as np
import ClearMap.Visualization.Plot3d as p3d
import ClearMap.Tests.Files as tsf
import ClearMap.ImageProcessing.Experts.Vasculature as vasc
source = np.array(tsf.source('vls')[:300,:300,80:120]);
source[:,:,[0,-1]] = 0;
source[:,[0,-1],:] = 0;
source[[0,-1],:,:] = 0;
bpar = vasc.default_binarization_parameter.copy();
bpar['clip']['clip_range'] = (150, 7000)
bpar['as_memory'] = True
#bpar['binary_status'] = 'binary_status.npy'
ppar = vasc.default_processing_parameter.copy();
ppar['processes'] = 10;
ppar['size_max'] = 10;
sink='binary.npy'
#sink=None;
binary = vasc.binarize(source, sink=sink, binarization_parameter=bpar, processing_parameter = ppar)
p3d.plot([source, binary])
import ClearMap.IO.IO as io
io.delete_file(sink)
pppar = vasc.default_postprocessing_parameter.copy();
pppar['smooth']['iterations'] = 3;
smoothed = vasc.postprocess(binary, postprocessing_parameter=pppar)
p3d.plot([binary, smoothed])
def _test(): import numpy as np import ClearMap.ImageProcessing.Binary.Smoothing as sm #reload(sm) lut = sm.initialize_lookup_table() #analysis:ignore #lut = sm.generate_lookup_table(verbose=True); shape = (30,40,50); binary = np.zeros(shape, dtype = bool, order='F'); grid = np.meshgrid(*[range(s) for s in shape], indexing='ij'); center = tuple(s/2 for s in shape); distance = np.sum([(g-c)**2 for g,c in zip(grid, center)], axis=0); binary[distance <= 10**2] = True import scipy.ndimage as ndi border = ndi.convolve(np.asarray(binary, dtype=int), np.ones((3,3,3))) border = np.logical_and(border > 0, border < 27) noisy = binary.copy(); noisy[np.logical_and(border, np.random.rand(*shape) > 0.925)] = True smoothed = sm.smooth_by_configuration(noisy, iterations=3, verbose=True, processes='serial') import ClearMap.Visualization.Plot3d as p3d p3d.plot([noisy, smoothed])
def _test():
import numpy as np
import ClearMap.ImageProcessing.GreyReconstruction as gr
import ClearMap.Visualization.Plot3d as p3d
x = np.random.rand(*(200, 200, 10))
r = gr.grey_reconstruct(x, mask=0.5 * x, method='erosion', shape=3)
p3d.plot([x, r])
def _test():
import ClearMap.ImageProcessing.Filter.StructureElement as se
from importlib import reload
reload(se)
d = se.sphere((150, 50, 10))
import ClearMap.Visualization.Plot3d as p3d
p3d.plot(d)
def _test():
"""Tests"""
import ClearMap.ImageProcessing.Filter.FilterKernel as fk
import ClearMap.Visualization.Plot3d as p3d
k = fk.filter_kernel(ftype='dog',
shape=(15, 15, 15),
sigma=None,
radius=None,
sigma2=None)
p3d.plot(k)
def _test():
import numpy as np
import ClearMap.Visualization.Plot3d as p3d
import ClearMap.ImageProcessing.Thresholding.Thresholding as th
r = np.arange(50)
x, y, z = np.meshgrid(r, r, r)
x, y, z = [i - 25 for i in (x, y, z)]
d = np.exp(-(x * x + y * y + z * z) / 10.0**2)
t = th.threshold(d, sink=None, threshold=0.9, hysteresis_threshold=0.5)
p3d.plot([[d, t]])
def _test():
"""Tests."""
import numpy as np
import ClearMap.Visualization.Plot3d as p3d
import ClearMap.ImageProcessing.LocalStatistics as ls
from importlib import reload
reload(ls)
source = np.random.rand(100, 200, 150) + np.arange(100)[:, None, None]
p = ls.local_percentile(source,
percentile=0.5,
selem=(30, 30, 30),
interpolate=1)
p3d.plot([source, p])
def test():
import numpy as np
import ClearMap.IO.IO as io
import ClearMap.Analysis.Measurements.MeasureRadius as mr
data = 10 - np.abs(10 - np.arange(0, 21))
search = mr.search_indices_sphere(radius=[10, 10, 10])
print(search)
points = np.array([10])
d, i = mr.measure_radius(data,
points,
fraction=0.75,
max_radius=10,
scale=2,
verbose=True,
processes=4,
return_indices=True)
data = np.random.rand(*(30, 40, 50))
io.write('data.npy', data)
points = np.array([np.random.randint(0, s, size=10) for s in data.shape]).T
d, i = mr.measure_radius(data,
points,
value=0.5,
max_radius=10,
scale=2,
verbose=True,
processes=4,
return_indices=True)
data = np.zeros((30, 40, 50), dtype=int)
data[10:20, 15:25, 10:20] = 1
data[15, 20, 15] = 2
import ClearMap.Visualization.Plot3d as p3d
p3d.plot(data)
points = np.array([[15, 20, 15], [4, 4, 4]])
d, i = mr.measure_radius(data,
points,
value=0.0,
max_radius=10,
scale=None,
verbose=True,
processes=None,
return_indices=True)
def _test():
"""Tests."""
import numpy as np
import ClearMap.Visualization.Plot3d as p3d
import ClearMap.ImageProcessing.Binary.Filling as bf
from importlib import reload
reload(bf)
test = np.zeros((50, 50, 50), dtype=bool)
test[20:30, 30:40, 25:35] = True
test[25:27, 34:38, 27:32] = False
test[5:15, 5:15, 23:35] = True
test[8:12, 8:12, 27:32] = False
filled = bf.fill(test, sink=None, processes=10)
p3d.plot([test, filled])
def _test():
import numpy as np
import ClearMap.IO.IO as io
import ClearMap.Visualization.Plot3d as p3d
import ClearMap.Tests.Files as tsf
import ClearMap.ImageProcessing.Skeletonization.PK12 as PK12
from importlib import reload
reload(PK12)
#Lookup tables
#lut = PK12.generate_lookup_table();
#np.save(PK12.filename, lut);
#lut.sum()
#lutnr = PK12.generate_lookup_table(function=PK12.match_non_removable, verbose = True);
#np.save(PK12.filename_non_removable, lutnr);
#lutnr.sum()
#Skeletonization
reload(PK12)
binary = tsf.skeleton_binary
binary_array = np.array(io.as_source(binary))
#default version
skeleton = PK12.skeletonize(binary_array.copy(),
delete_border=True,
verbose=True)
p3d.plot([[binary_array, skeleton]])
#fast index version
skeleton = PK12.skeletonize_index(binary_array.copy(),
delete_border=True,
verbose=True)
p3d.plot([[binary_array, skeleton]])
# plotting
import ClearMap.Visualization.Plot3d as p3d
p3d.plot_3d(binary_array[:150, :150, :150], cmap=p3d.grays_alpha(0.05))
p3d.plot_3d(skeleton[:150, :150, :150],
cmap=p3d.single_color_colormap('red', alpha=0.8))
#save for figure
import scipy.io as sio
sio.savemat('binary.mat', {'binary': binary_array[:100, :100, :100]})
sio.savemat('binary_skeleton.mat',
{'skeleton': skeleton[:100, :100, :100]})
def _test():
import numpy as np
import ClearMap.Visualization.Plot3d as p3d
import ClearMap.ImageProcessing.Filter.SeedThresholding.SeedThresholding as sth
from importlib import reload
reload(sth)
r = np.arange(50)
x, y, z = np.meshgrid(r, r, r)
x, y, z = [i - 25 for i in (x, y, z)]
d = np.exp(-(x * x + y * y + z * z) / 10.0**2)
m = sth.local_max(d, shape=(3, 3, 3))
x, y, z = np.where(m)
t = sth.threshold(d,
sink=None,
seeds_x=x,
seeds_y=y,
seeds_z=z,
percentage=0.5,
absolute=0.2)
p3d.plot([[d, m, t]])
import ClearMap.Test.Files as tst
d = tst.init('v')[:200, :200, :100]
import ClearMap.ImageProcessing.Filter.Rank as rnk
d = np.array(d)
d = rnk.median(d, np.ones((3, 3, 3), dtype=bool))
m = sth.local_max(d, shape=(5, 5, 1))
m = np.logical_and(m, d > 10)
x, y, z = np.where(m)
v = d[x, y, z]
s = np.argsort(v)[::-1]
x, y, z = [i[s] for i in (x, y, z)]
t = sth.threshold(d,
sink=None,
seeds_x=x,
seeds_y=y,
seeds_z=z,
percentage=0.25,
absolute=5)
p3d.plot([d, [d, m, t]])
def _test():
"""Test the statistics array"""
import numpy as np
import ClearMap.Analysis.Statistics.GroupStatistics as st
s = np.ones((5, 4, 20))
s[:, 0:3, :] = -1
x = np.random.rand(4, 4, 20)
y = np.random.rand(5, 4, 20) + s
pvals, psign = st.t_test_voxelization(x, y, signed=True)
pvalscol = st.color_p_values(pvals,
psign,
positive=[255, 0, 0],
negative=[0, 255, 0])
import ClearMap.Visualization.Plot3d as p3d
p3d.plot(pvalscol)
def _test():
import numpy as np
import ClearMap.IO.IO as io
import ClearMap.ImageProcessing.Skeletonization.Skeletonization as skl
import ClearMap.Visualization.Plot3d as p3d
import ClearMap.Tests.Files as tsf
from importlib import reload
reload(skl)
binary = tsf.skeleton_binary
#default version
skeleton = skl.skeletonize(binary, delete_border=True, verbose=True)
p3d.plot([[binary, skeleton]])
#fast index version
skeleton = skl.PK12.skeletonize_index(binary.copy(),
delete_border=True,
verbose=True)
p3d.plot([[binary, skeleton]])
def plotTracingResult(path,
data_nbh,
mask_nbh,
center,
radius,
tubeness_nbh,
skeleton=None,
distance_nbh=None):
global viewer
img_nbh = np.asarray(data_nbh, dtype=float)
bin_nbh = np.zeros(mask_nbh.shape, dtype=int)
if skeleton is not None:
bin_nbh += extractNeighbourhood(skeleton, center, radius)
addPathToMask(bin_nbh, path, value=4)
if points is not None:
pts = points - center + radius
for d in range(3):
ids = np.logical_and(0 <= pts[:, d], pts[:, d] < data_nbh.shape[d])
pts = pts[ids]
addPathToMask(bin_nbh, pts, value=3)
addPathToMask(img_nbh, path, value=512)
addPathToMask(tubeness_nbh, path, value=50)
if distance_nbh is not None:
addPathToMask(distance_nbh, path, value=50)
try:
viewer[0].setSource(bin_nbh)
viewer[1].setSource(img_nbh)
viewer[2].setSource(tubeness_nbh)
if distance_nbh is not None:
viewer[3].setSource(distance_nbh)
except:
if distance_nbh is not None:
viewer = p3d.plot([bin_nbh, img_nbh, tubeness_nbh, distance_nbh])
else:
viewer = p3d.plot([bin_nbh, img_nbh, tubeness_nbh])
def _test():
"""Tests"""
import numpy as np
import ClearMap.Visualization.Plot3d as p3d
import ClearMap.ParallelProcessing.DataProcessing.DevolvePointList as dpl
from importlib import reload
reload(dpl)
points = np.array([1, 9, 10], dtype=float)
indices = [-2, -1, 0, 1]
v = dpl.devolve(points, shape=(11, ), indices=indices)
print(v.array)
points = np.array([[10, 10, 18]])
indices = [[-1, -1, -1], [0, 0, 0], [1, 1, 1]]
v = dpl.devolve(points, shape=(20, 20, 20), indices=indices, weights=None)
p3d.plot(v)
points = np.array([[10, 10, 10]])
weights = np.random.rand(len(points))
v = dpl.devolve(points,
shape=(20, 20, 20),
indices=indices,
weights=weights)
p3d.plot(v)
kernel = np.random.rand(len(indices))
v = dpl.devolve(points,
shape=(20, 20, 20),
indices=indices,
weights=None,
kernel=kernel)
p3d.plot(v)
def plotData(data, skel, binary, ends=None, isolated=None, replot=True):
global viewer
img = np.zeros(data.shape, dtype=int)
img[:] = img + binary + skel
if ends is not None:
xyz = np.array(np.unravel_index(ends, skel.shape, order='C')).T
for x, y, z in xyz:
img[x, y, z] = 3
if isolated is not None:
xyz = np.array(np.unravel_index(isolated, skel.shape, order='C')).T
for x, y, z in xyz:
img[x, y, z] = 4
if replot:
try:
viewer[0].setSource(data)
viewer[1].setSource(data)
except:
viewer = p3d.plot([data, img])
else:
return p3d.plot([data, img])
def _test():
"""Tests"""
import numpy as np
import ClearMap.Visualization.Plot3d as p3d
import ClearMap.Analysis.Measurements.Voxelization as vox
#points = np.random.rand(20,3) * 15;
#points = np.asarray(points, dtype=int);
points = np.array([[10, 1, 1]])
v = vox.voxelize(points, shape=(20, 20, 20), radius=(0, 0, 0))
w = vox.voxelize(points, shape=(20, 20, 20), radius=(2, 2, 2))
p3d.plot([[v, w]])
indices, kernel = vox.search_indices_sphere((2, 2, 2), kernel=None)
print(indices)
points = np.array([[10, 10, 18]])
v = vox.voxelize(points,
shape=(20, 20, 20),
weights=None,
radius=(5, 7, 10))
p3d.plot(v)
points = np.array([[10, 10, 10]])
weights = np.random.rand(len(points))
v = vox.voxelize(points,
shape=(20, 20, 20),
weights=weights,
radius=(2, 2, 2),
method='rectangle')
p3d.plot(v)
def kernel(d):
return np.exp(-d)
v = vox.voxelize(points,
shape=(20, 20, 20),
weights=None,
radius=(5, 5, 8),
kernel=kernel,
method='sphere')
p3d.plot(v)
reload(sth)
r = np.arange(50)
x, y, z = np.meshgrid(r, r, r)
x, y, z = [i - 25 for i in (x, y, z)]
d = np.exp(-(x * x + y * y + z * z) / 10.0**2)
m = sth.local_max(d, shape=(3, 3, 3))
x, y, z = np.where(m)
t = sth.threshold(d,
sink=None,
seeds_x=x,
seeds_y=y,
seeds_z=z,
percentage=0.5,
absolute=0.2)
p3d.plot([[d, m, t]])
import ClearMap.Test.Files as tst
d = tst.init('v')[:200, :200, :100]
import ClearMap.ImageProcessing.Filter.Rank as rnk
d = np.array(d)
d = rnk.median(d, np.ones((3, 3, 3), dtype=bool))
m = sth.local_max(d, shape=(5, 5, 1))
m = np.logical_and(m, d > 10)
x, y, z = np.where(m)
v = d[x, y, z]
s = np.argsort(v)[::-1]
x, y, z = [i[s] for i in (x, y, z)]
t = sth.threshold(d,
sink=None,
def _test():
import numpy as np
import scipy.ndimage as ndi
import ClearMap.Visualization.Plot3d as p3d
import ClearMap.ImageProcessing.Filter.Curvature.Curvature as cur
import ClearMap.ImageProcessing.Tracing.Trace as trc
from importlib import reload
reload(trc)
#x = np.random.rand(50,50,50);
#x = ndi.gaussian_filter(x, sigma = 2);
x = np.load('/home/ckirst/Desktop/data.npy')[:50, :50, :50]
x = np.ascontiguousarray(x, dtype=float)
start, stop = [37, 20, 24], [27, 32, 38]
# from collections import defaultdict
# import gc
# gc.collect();
#before = defaultdict(int)
#after = defaultdict(int)
#for i in gc.get_objects():
# before[type(i)] += 1
t = cur.tubeness(x)
#reload(trc);
p = trc.trace(x, t, start, stop, verbose=False)
#gc.collect();
#for i in gc.get_objects():
# after[type(i)] += 1
#print [(k, after[k] - before[k]) for k in after if after[k] - before[k]]
#import mem_top as mt;
#print mt.mem_top(width = 150);
#%%
xp = np.asarray(x, dtype=int)
for pp in p:
xp[pp[0], pp[1], pp[2]] = 512
try:
dd[0].setSource(xp)
dd[1].setSource(t)
except:
dd = p3d.plot([xp, t])
import scipy.ndimage as ndi
mask = np.zeros_like(x)
mask[:, :, :40] = 1
# zeros are the goal !!
dist = ndi.distance_transform_edt(mask, sampling=[1, 1, 1])
start = [37, 20, 25]
path = trc.traceToMask(x, t, start, dist)
xp = np.asarray(x, dtype=int)
for pp in path:
xp[pp[0], pp[1], pp[2]] = 512
try:
dd[0].setSource(xp)
dd[1].setSource(t)
except:
dd = p3d.plot([xp, t])
def _test():
"""Tests"""
import ClearMap.Settings as settings
import ClearMap.IO.IO as io
import ClearMap.Visualization.Plot3d as p3d;
import ClearMap.Alignment.Resampling as res
from importlib import reload
reload(res)
r = res.resample_shape(source_shape=(100,200,300), sink_shape=(50,50,30));
print('resampled source_shape=%r, sink_shape=%r, source_resolution=%r, sink_resolution=%r' % r)
r = res.resample_shape(source_shape=(100,200,300), source_resolution=(2,2,2), sink_resolution=(10,2,1))
print('resampled source_shape=%r, sink_shape=%r, source_resolution=%r, sink_resolution=%r' % r)
source = io.join(settings.test_data_path, 'Resampling/test.tif')
sink = io.join(settings.test_data_path, "Resampling/resampled.npy")
source = io.join(settings.test_data_path, 'Tif/sequence/sequence<Z,4>.tif')
sink = io.join(settings.test_data_path, "Resampling/resampled_sequence.tif")
source_shape, sink_shape, source_res, sink_res = res.resample_shape(source_shape=io.shape(source), source_resolution=(1.,1.,1.), sink_resolution=(1.6,1.6,2))
axes_order = res._axes_order(None, source_shape, sink_shape);
print(axes_order)
resampled = res.resample(source, sink, source_resolution=(1.,1.,1.), sink_resolution = (1.6,1.6,2), orientation=None, processes=None);
p3d.plot(resampled)
p3d.plot(source)
inverse = res.resample_inverse(resampled, sink=None, resample_source=source, source_resolution=(1,1,1), sink_resolution = (10,10,2), orientation=None, processes='serial')
p3d.plot([source, inverse])
resampled = res.resample(source, sink, source_resolution=(1,1,1), sink_resolution = (10,10,2), orientation=(2,-1,3), processes=None);
p3d.plot(resampled)
inverse = res.resample_inverse(resampled, sink=None, resample_source=source, source_resolution=(1,1,1), sink_resolution = (10,10,2), orientation=(2,-1,3), processes=None)
p3d.plot([source, inverse])
resampled = res.resample(source, sink=None, source_resolution=(1.6,1.6,2), sink_shape=(10,20,30), orientation=None, processes=None)
p3d.plot(resampled)
# ponints
points = res.np.array([[0,0,0], [1,1,1], [1,2,3]], dtype=float);
resampled_points = res.resample_points(points, resample_source=source , resample_sink=sink, orientation=None)
print(resampled_points)
inverse_points = res.resample_points_inverse(resampled_points, resample_source=source , resample_sink=sink, orientation=None)
print(inverse_points)
print(res.np.allclose(points, inverse_points))
def test():
"""Test voxelization module"""
import numpy as np
import ClearMap.Analysis.Orientation as ort
from importlib import reload
reload(ort)
n = 200
#points = np.random.rand(n,3) * 20;
#orientations = np.random.rand(n,3);
#orientations = (orientations.T / np.linalg.norm(orientations, axis = 1)).T;
phi = np.linspace(0, 2 * np.pi, n)
psi = np.linspace(0, 2 * np.pi, n)
phi, psi = np.meshgrid(phi, psi)
orientations = np.array(
[np.cos(psi) * np.cos(phi),
np.sin(phi),
np.sin(psi) * np.cos(phi)])
points = np.meshgrid(np.arange(n), np.arange(n))
points = np.array(points)
ox, oy, oz = orientations
px, py = points
orientations_f = np.array([ox.flatten(), oy.flatten(), oz.flatten()]).T
points_f = np.array([px.flatten(), py.flatten()]).T
import mayavi.mlab as mlab
mlab.quiver3d(points_f[:, 0],
points_f[:, 1],
np.zeros(points_f.shape[0]),
orientations_f[:, 0],
orientations_f[:, 1],
orientations_f[:, 2],
color='black',
opacity=0.2,
scale_factor=1.5)
vo = ort.voxelizeOrientations(points_f,
orientations_f,
dataSize=(20, 20, 20),
size=(5, 5, 5))
#normalize
nrm = np.linalg.norm(vo, axis=-1)
nrm[nrm < 10e-5] = 1.0
vo = (vo.transpose((3, 0, 1, 2)) / nrm).transpose((1, 2, 3, 0))
import ClearMap.Visualization.ColorMaps as cmaps
reload(cmaps)
vo2 = cmaps.boys2rgb(vo.transpose([3, 0, 1, 2]))
import ClearMap.Visualization.Plot3d as p3d
p3d.plot([[vo2[:, :, :, 0], vo2[:, :, :, 1], vo2[:, :, :, 2]]])
import ClearMap.Visualization.ColorMaps as cmaps
reload(cmaps)
ob = cmaps.boys2rgb(orientations)
ob.shape = ob.shape + (1, )
p3d.plot([[ob[0], ob[1], ob[2]]])
from mayavi import mlab
import numpy as np
import ClearMap.Visualization.ColorMaps as cmaps
reload(cmaps)
# Make sphere, choose colors
phi, theta = np.mgrid[0:np.pi:101j, 0:2 * np.pi:101j]
x, y, z = np.sin(phi) * np.cos(theta), np.sin(phi) * np.sin(theta), np.cos(
phi)
xr, yr, zr = x.ravel(), y.ravel(), z.ravel()
rgb = cmaps.boys2rgb([xr, yr, zr])
rgb = np.abs(np.array([xr, yr, zr])).T
color = np.zeros((rgb.shape[0], 4), dtype='uint8')
color[:, :3] = rgb * 255
color[:, 3] = 255
#s = cmaps.rgbToLUT(rgb);
#si = np.array(s, dtype = int);
si = np.arange(len(xr))
si.shape = x.shape
#lut = cmaps.rgbLUT();
lut = 0.8 * color
lut[:, 3] = 255
# Display
mlab.figure(1, bgcolor=(1, 1, 1), fgcolor=(0, 0, 0), size=(600, 500))
mm = mlab.mesh(x,
y,
z,
scalars=si,
colormap='Spectral',
vmin=0,
vmax=lut.shape[0])
#mm = mlab.points3d(xr, yr, zr, si, mode = 'point')
#magic to modify lookup table
mm.module_manager.scalar_lut_manager.lut._vtk_obj.SetTableRange(
0, lut.shape[0])
mm.module_manager.scalar_lut_manager.lut.number_of_colors = lut.shape[0]
mm.module_manager.scalar_lut_manager.lut.table = lut
mlab.view()
mlab.show()
def _test():
#%%
import numpy as np
import scipy.ndimage as ndi
import ClearMap.DataProcessing.LargeData as ld
import ClearMap.Visualization.Plot3d as p3d
import ClearMap.DataProcessing.ConvolvePointList as cpl
import ClearMap.ImageProcessing.Skeletonization.Topology3d as t3d
import ClearMap.ImageProcessing.Skeletonization.SkeletonCleanUp as scu
import ClearMap.ImageProcessing.Tracing.Connect as con
reload(con)
data = np.load('/home/ckirst/Desktop/data.npy')
binary = np.load('/home/ckirst/Desktop/binarized.npy')
skel = np.load('/home/ckirst/Desktop/skel.npy')
#points = np.load('/home/ckirst/Desktop/pts.npy');
data = np.copy(data, order='F')
binary = np.copy(binary, order='F')
skel = np.copy(skel, order='F')
skel_copy = np.copy(skel, order='F')
points = np.ravel_multi_index(np.where(skel), skel.shape, order='F')
skel, points = scu.cleanOpenBranches(skel,
skel_copy,
points,
length=3,
clean=True)
deg = cpl.convolve3DIndex(skel, t3d.n26, points)
ends, isolated = con.findEndpoints(skel, points, border=25)
special = np.sort(np.hstack([ends, isolated]))
ends_xyz = np.array(np.unravel_index(ends, data.shape, order='F')).T
isolated_xyz = np.array(np.unravel_index(isolated, data.shape,
order='F')).T
special_xyz = np.vstack([ends_xyz, isolated_xyz])
#%%
import ClearMap.ParallelProcessing.SharedMemoryManager as smm
data_s = smm.asShared(data, order='F')
binary_s = smm.asShared(binary.view('uint8'), order='F')
skel_s = smm.asShared(skel.view('uint8'), order='F')
smm.clean()
res = con.addConnections(data_s,
binary_s,
skel_s,
points,
radius=20,
start_points=None,
add_to_skeleton=True,
add_to_mask=True,
verbose=True,
processes=4,
debug=False,
block_size=10)
skel_s = skel_s.view(bool)
binary_s = binary_s.view(bool)
#%%
mask_img = np.asarray(binary, dtype=int, order='A')
mask_img[:] = mask_img + binary_s
mask_img[:] = mask_img + skel
data_img = np.copy(data, order='A')
data_img[skel] = 120
mask_img_f = np.reshape(mask_img, -1, order='A')
data_img_f = np.reshape(data_img, -1, order='A')
mask_img_f[res] = 7
data_img_f[res] = 512
mask_img_f[special] = 8
data_img_f[special] = 150
for d in [3, 4, 5]:
mask_img_f[points[deg == d]] = d + 1
try:
con.viewer[0].setSource(mask_img)
con.viewer[1].setSource(data_img)
except:
con.viewer = p3d.plot([mask_img, data_img])
con.viewer[0].setMinMax([0, 8])
con.viewer[1].setMinMax([24, 160])
#%%
mask = binary
data_new = np.copy(data, order='A')
data_new[skel] = 120
skel_new = np.asarray(skel, dtype=int, order='A')
skel_new[:] = skel_new + binary
binary_new = np.copy(binary, order='A')
qs = []
for i, e in enumerate(special):
print('------')
print('%d / %d' % (i, len(special)))
path, quality = con.connectPoint(data,
mask,
special,
i,
radius=25,
skeleton=skel,
tubeness=None,
remove_local_mask=True,
min_quality=15.0,
verbose=True,
maxSteps=15000,
costPerDistance=1.0)
#print path, quality
if len(path) > 0:
qs.append(quality * 1.0 / len(path))
q = con.addPathToMask(skel_new, path, value=7)
q = con.addPathToMask(data_new, path, value=512)
binary_new = con.addDilatedPathToMask(binary_new,
path,
iterations=1)
skel_new[:] = skel_new + binary_new
q = con.addPathToMask(skel_new, special_xyz, value=6)
for d in [3, 4, 5]:
xyz = np.array(
np.unravel_index(points[deg == d], data.shape, order='F')).T
q = con.addPathToMask(skel_new, xyz, value=d)
q = con.addPathToMask(data_new, special_xyz, value=150)
try:
con.viewer[0].setSource(skel_new)
con.viewer[1].setSource(data_new)
except:
con.viewer = p3d.plot([skel_new, data_new])
con.viewer[0].setMinMax([0, 8])
con.viewer[1].setMinMax([24, 160])
#%%
import matplotlib.pyplot as plt
plt.figure(1)
plt.clf()
#plt.plot(qs);
plt.hist(qs)
#%%
i = 20
i = 21
i = 30
i = 40
r = 25
center = np.unravel_index(ends[i], data.shape)
print(center, data.shape)
mask = binary
path = con.tracePointToMask(data,
mask,
center,
radius=r,
points=special_xyz,
plot=True,
skel=skel,
binary=binary,
tubeness=None,
removeLocalMask=True,
maxSteps=None,
verbose=False,
costPerDistance=0.0)
#%%
nbs = ap.findNeighbours(ends, i, skel.shape, skel.strides, r)
center = np.unravel_index(ends[i], skel.shape)
nbs_xyz = np.array(np.unravel_index(nbs, skel.shape)).T
dists = nbs_xyz - center
dists = np.sum(dists * dists, axis=1)
nb = np.argmin(dists)
center = np.unravel_index(ends[i], data.shape)
print(center, data.shape)
mask = binary
path = con.tracePointToNeighbor(data,
mask,
center,
nbs_xyz[nb],
radius=r,
points=special_xyz,
plot=True,
skel=skel,
binary=binary,
tubeness=None,
removeLocalMask=True,
maxSteps=None,
verbose=False,
costPerDistance=0.0)
#%%
import ClearMap.ImageProcessing.Filter.FilterKernel as fkr
dog = fkr.filterKernel('DoG', size=(13, 13, 13))
dv.plot(dog)
data_filter = ndi.correlate(np.asarray(data, dtype=float), dog)
data_filter -= data_filter.min()
data_filter = data_filter / 3.0
#dv.dualPlot(data, data_filter);
#%%add all paths
reload(con)
r = 25
mask = binary
data_new = data.copy()
data_new[skel] = 120
skel_new = np.asarray(skel, dtype=int)
skel_new = skel_new + binary
binary_new = binary.copy()
for i, e in enumerate(special):
center = np.unravel_index(e, data.shape)
print(i, e, center)
path = con.tracePointToMask(data,
mask,
center,
radius=r,
points=special_xyz,
plot=False,
skel=skel,
binary=binary,
tubeness=None,
removeLocalMask=True,
maxSteps=15000,
costPerDistance=1.0)
q = con.addPathToMask(skel_new, path, value=7)
q = con.addPathToMask(data_new, path, value=512)
binary_new = con.addDilatedPathToMask(binary_new, path, iterations=1)
q = con.addPathToMask(skel_new, special_xyz, value=6)
for d in [3, 4, 5]:
xyz = np.array(np.unravel_index(points[deg == d], data.shape)).T
q = con.addPathToMask(skel_new, xyz, value=d)
q = con.addPathToMask(data_new, special_xyz, value=150)
skel_new = skel_new + binary_new
try:
con.viewer[0].setSource(skel_new)
con.viewer[1].setSource(data_new)
except:
con.viewer = dv.dualPlot(skel_new, data_new)
con.viewer[0].setMinMax([0, 8])
con.viewer[1].setMinMax([24, 160])
#%%
import ClearMap.ImageProcessing.Skeletonization.Skeletonize as skl
skel_2 = skl.skeletonize3D(binary_new.copy())
#%%
np.save('/home/ckirst/Desktop/binarized_con.npy', binary_new)
#%%
# write image
import ClearMap.IO.IO as io
#r = np.asarray(128 * binary_new, dtype = 'uint8');
#g = r.copy(); b = r.copy();
#r[:] = r + 127 * skel_2[0];
#g[:] = g - 128 * skel_2[0];
#b[:] = b - 128 * skel_2[0];
#img = np.stack((r,g,b), axis = 3)
img = np.asarray(128 * binary_new, dtype='uint8')
img[:] = img + 127 * skel_2[0]
io.writeData('/home/ckirst/Desktop/3d.tif', img)
def _test():
import numpy as np
import ClearMap.ParallelProcessing.DataProcessing.ArrayProcessing as ap
## Lookup table processing
#apply_lut
x = np.random.randint(0, 100, size=(20, 30))
lut = np.arange(100) + 1
y = ap.apply_lut(x, lut)
assert np.all(y == x + 1)
#apply_lut_to_index
import ClearMap.ImageProcessing.Topology.Topology3d as t3d
kernel = t3d.index_kernel(dtype=int)
import ClearMap.ImageProcessing.Binary.Smoothing as sm
lut = sm.initialize_lookup_table()
data = np.array(np.random.rand(150, 30, 40) > 0.75, order='F')
result = ap.apply_lut_to_index(data, kernel, lut, sink=None, verbose=True)
import ClearMap.Visualization.Plot3d as p3d
p3d.plot([[data, result]])
### Correlation
#correlate1d
kernel = np.array(range(11), dtype='uint32')
data = np.array(np.random.randint(0,
2**27, (300, 400, 1500),
dtype='uint32'),
order='F')
#data = np.array(np.random.rand(3,4,5), order='F');
data = np.empty((300, 400, 1500), order='F')
kernel = np.array([1, 2, 3, 4, 5], dtype='uint8')
sink = 'test.npy'
import ClearMap.Utils.Timer as tmr
import scipy.ndimage as ndi
timer = tmr.Timer()
for axis in range(3):
print(axis)
corr_ndi = ndi.correlate1d(data, axis=axis, mode='constant', cval=0)
timer.print_elapsed_time('ndi')
timer = tmr.Timer()
for axis in range(3):
print(axis)
corr = ap.correlate1d(data,
sink=sink,
kernel=kernel,
axis=axis,
verbose=False,
processes=None)
timer.print_elapsed_time('ap')
assert np.allclose(corr.array, corr_ndi)
# IO
import ClearMap.ParallelProcessing.DataProcessing.ArrayProcessing as ap
import numpy as np
reload(ap)
data = np.random.rand(10, 200, 10)
sink = ap.write('test.npy', data, verbose=True)
assert (np.all(sink.array == data))
read = ap.read('test.npy', verbose=True)
assert (np.all(read.array == data))
ap.io.delete_file('test.npy')
# where
reload(ap)
data = np.random.rand(30, 20, 40) > 0.5
where_np = np.array(np.where(data)).T
where = ap.where(data, cutoff=2**0)
check_np = np.zeros(data.shape, dtype=bool)
check = np.zeros(data.shape, dtype=bool)
check_np[tuple(where_np.T)] = True
check[tuple(where.array.T)] = True
assert (np.all(check_np == check))
def plot(self, ftype, **kwargs): return p3d.plot(self.filename(ftype, **kwargs));
def _test():
import numpy as np
import ClearMap.ParallelProcessing.DataProcessing.ArrayProcessing as ap
from importlib import reload
reload(ap)
## Lookup table processing
#apply_lut
x = np.random.randint(0, 100, size=(20,30));
lut = np.arange(100) + 1;
y = ap.apply_lut(x, lut)
assert np.all(y == x+1)
#apply_lut_to_index
import ClearMap.ImageProcessing.Topology.Topology3d as t3d
kernel = t3d.index_kernel(dtype=int);
import ClearMap.ImageProcessing.Binary.Smoothing as sm
lut = sm.initialize_lookup_table()
data = np.random.randint(0, 2, (1500,300,400), dtype = bool)
#reload(ap)
result = ap.apply_lut_to_index(data, kernel, lut, sink=None, verbose=True)
import ClearMap.Visualization.Plot3d as p3d
p3d.plot([data, result])
## Correlation
#correlate1d
#reload(ap)
axis = 1;
kernel = np.array(range(11), dtype='uint32');
data = np.random.randint(0, 2**27, (1000, 1500,100), dtype='uint32');
corr = ap.correlate1d(data, kernel, axis=axis, verbose=True, processes=10);
import scipy.ndimage as ndi
import ClearMap.Utils.Timer as tmr
timer = tmr.Timer();
corr_ndi = ndi.correlate1d(data, kernel, axis=axis, mode='constant',cval=0);
timer.print_elapsed_time('ndi')
assert np.allclose(corr, corr_ndi)
#
#
#
#
#
#default_blocks_per_process = 10;
#"""Default number of blocks per process to split the data.
#
#Note
#----
#10 blocks per process is a good choice.
#"""
#
#default_cutoff = 20000000;
#"""Default size of array below which ordinary numpy is used.
#
#Note
#----
#Ideally test this on your machine for different array sizes.
#"""
#
#
#
#def blockRanges(data, blocks = None, processes = defaultProcesses):
# """Ranges of evenly spaced blocks in array
#
# Arguments:
# data : array
# array to divide in blocks
# blocks : int or None
# number of blocks to split array into
# processes : None or int
# number of processes, if None use number of cpus
#
# Returns:
# array
# list of the range boundaries
# """
# if processes is None:
# processes = defaultProcesses;
# if blocks is None:
# blocks = processes * defaultBlocksPerProcess;
#
# d = data.reshape(-1, order = 'A');
# blocks = min(blocks, d.shape[0]);
# return np.array(np.linspace(0, d.shape[0], blocks + 1), dtype = int);
#
#
#def blockSums(data, blocks = None, processes = defaultProcesses):
# """Sums of evenly spaced blocks in array
#
# Arguments:
# data : array
# array to perform the block sums on
# blocks : int or None
# number of blocks to split array into
# processes : None or int
# number of processes, if None use number of cpus
#
# Returns:
# array
# sums of the values in the different blocks
# """
# if processes is None:
# processes = defaultProcesses;
# if blocks is None:
# blocks = processes * defaultBlocksPerProcess;
#
# d = data.reshape(-1, order = 'A');
# if data.dtype == bool:
# d = d.view('uint8')
#
# return code.blockSums1d(d, blocks = blocks, processes = processes);
#
#
#def where(data, out = None, blocks = None, cutoff = defaultCutoff, processes = defaultProcesses):
# """Returns the indices of the non-zero entries of the array
#
# Arguments:
# data : array
# array to search for nonzero indices
# out : array or None
# if not None results is written into this array
# blocks : int or None
# number of blocks to split array into for parallel processing
# cutoff : int
# number of elements below whih to switch to numpy.where
# processes : None or int
# number of processes, if None use number of cpus
#
# Returns:
# array
# positions of the nonzero entries of the input array
#
# Note:
# Uses numpy.where if there is no match of dimension implemented!
# """
# if data.ndim != 1 and data.ndim != 3:
# raise Warning('Using numpy where for dimension %d and type %s!' % (data.ndim, data.dtype))
# return np.vstack(np.where(data)).T;
#
# if cutoff is None:
# cutoff = 1;
# cutoff = min(1, cutoff);
# if data.size <= cutoff:
# return np.vstack(np.where(data)).T;
#
# if processes is None:
# processes = defaultProcesses;
# if blocks is None:
# blocks = processes * defaultBlocksPerProcess;
#
# if data.dtype == bool:
# d = data.view('uint8')
# else:
# d = data;
#
# if out is None:
# if d.ndim == 1:
# sums = code.blockSums1d(d, blocks = blocks, processes = processes);
# else:
# sums = code.blockSums3d(d, blocks = blocks, processes = processes);
# out = np.squeeze(np.zeros((np.sum(sums), data.ndim), dtype = np.int));
# else:
# sums = None;
#
# if d.ndim == 1:
# code.where1d(d, out = out, sums = sums, blocks = blocks, processes = processes);
# else: # d.ndim == 3:
# code.where3d(d, out = out, sums = sums, blocks = blocks, processes = processes);
#
# return out;
#
#
#
#
#def setValue(data, indices, value, cutoff = defaultCutoff, processes = defaultProcesses):
# """Set value at specified indices of an array
#
# Arguments:
# data : array
# array to search for nonzero indices
# indices : array or None
# list of indices to set
# value : numeric or bool
# value to set elements in data to
# processes : None or int
# number of processes, if None use number of cpus
#
# Returns:
# array
# array with specified entries set to new value
#
# Note:
# Uses numpy if there is no match of dimension implemented!
# """
# if data.ndim != 1:
# raise Warning('Using numpy where for dimension %d and type %s!' % (data.ndim, data.dtype))
# data[indices] = value;
# return data;
#
# if cutoff is None:
# cutoff = 1;
# cutoff = min(1, cutoff);
# if data.size <= cutoff:
# data[indices] = value;
# return data;
#
# if processes is None:
# processes = defaultProcesses;
#
# if data.dtype == bool:
# d = data.view('uint8')
# else:
# d = data;
#
# code.set1d(d, indices, value, processes = processes);
#
# return data;
#
#
#def setArray(data, indices, values, cutoff = defaultCutoff, processes = defaultProcesses):
# """Set value at specified indices of an array
#
# Arguments:
# data : array
# array to search for nonzero indices
# indices : array or None
# list of indices to set
# values : array
# values to set elements in data to
# processes : None or int
# number of processes, if None use number of cpus
#
# Returns:
# array
# array with specified entries set to new value
#
# Note:
# Uses numpy if there is no match of dimension implemented!
# """
# if data.ndim != 1:
# raise Warning('Using numpy where for dimension %d and type %s!' % (data.ndim, data.dtype))
# data[indices] = values;
# return data;
#
# if cutoff is None:
# cutoff = 1;
# cutoff = min(1, cutoff);
# if data.size <= cutoff:
# data[indices] = values;
# return data;
#
# if processes is None:
# processes = defaultProcesses;
#
# if data.dtype == bool:
# d = data.view('uint8')
# else:
# d = data;
#
# code.set1darray(d, indices, values, processes = processes);
#
# return data;
#
#
#
#def take(data, indices, out = None, cutoff = defaultCutoff, processes = defaultProcesses):
# """Extracts the values at specified indices
#
# Arguments:
# data : array
# array to search for nonzero indices
# out : array or None
# if not None results is written into this array
# cutoff : int
# number of elements below whih to switch to numpy.where
# processes : None or int
# number of processes, if None use number of cpus
#
# Returns:
# array
# positions of the nonzero entries of the input array
#
# Note:
# Uses numpy data[indices] if there is no match of dimension implemented!
# """
# if data.ndim != 1:
# raise Warning('Using numpy where for dimension %d and type %s!' % (data.ndim, data.dtype))
# return data[indices];
#
# if cutoff is None:
# cutoff = 1;
# cutoff = min(1, cutoff);
# if data.size < cutoff:
# return data[indices];
#
# if processes is None:
# processes = defaultProcesses;
#
# if data.dtype == bool:
# d = data.view('uint8')
# else:
# d = data;
#
# if out is None:
# out = np.empty(len(indices), dtype = data.dtype);
# if out.dtype == bool:
# o = out.view('uint8');
# else:
# o = out;
#
# code.take1d(d, indices, o, processes = processes);
#
# return out;
#
#
#def match(match, indices, out = None):
# """Matches a sorted list of 1d indices to another larger one
#
# Arguments:
# match : array
# array of indices to match to indices
# indices : array or None
# array of indices
#
# Returns:
# array
# array with specified entries set to new value
#
# Note:
# Uses numpy if there is no match of dimension implemented!
# """
# if match.ndim != 1:
# raise ValueError('Match array dimension required to be 1d, found %d!' % (match.ndim))
# if indices.ndim != 1:
# raise ValueError('Indices array dimension required to be 1d, found %d!' % (indices.ndim))
#
# if out is None:
# out = np.empty(len(match), dtype = match.dtype);
#
# code.match1d(match, indices, out);
#
# return out;
#
#
# Find neighbours in an index list
#
#
#def neighbours(indices, offset, processes = defaultProcesses):
# """Returns all pairs of indices that are apart a specified offset"""
# return code.neighbours(indices, offset = offset, processes = processes);
#
#
#def findNeighbours(indices, center, shape, strides, mask):
# """Finds all indices within a specified kernel region centered at a point"""
#
# if len(strides) != 3 or len(shape) != 3 or (strides[0] != 1 and strides[2] != 1):
# raise RuntimeError('only 3d C or F contiguous arrays suported');
#
# if isinstance(mask, int):
# mask = (mask,);
# if isinstance(mask, tuple):
# mask = mask * 3;
# return code.neighbourlistRadius(indices, center, shape[0], shape[1], shape[2],
# strides[0], strides[1], strides[2],
# mask[0], mask[1], mask[2]);
# else:
# if mask.dtype == bool:
# mask = mask.view(dtype = 'uint8');
#
# return code.neighbourlistMask(indices, center, shape[0], shape[1], shape[2], strides[0], strides[1], strides[2], mask);
#
# Loading and saving
#
#def readNumpyHeader(filename):
# """Read numpy array information including offset to data
#
# Arguments:
# filename : str
# file name of the numpy file
#
# Returns:
# shape : tuple
# shape of the array
# dtype : dtype
# data type of array
# order : str
# 'C' for c and 'F' for fortran order
# offset : int
# offset in bytes to data buffer in file
# """
# with open(filename, 'rb') as fhandle:
# major, minor = np.lib.format.read_magic(fhandle);
# shape, fortran, dtype = np.lib.format.read_array_header_1_0(fhandle);
# offset = fhandle.tell()
#
# order = 'C';
# if fortran:
# order = 'F';
#
# return (shape, dtype, order, offset)
#
#
#def _offsetFromSlice(sourceSlice, order = 'F'):
# """Checks if slice is compatible with the large data loader and returns z coordiante"""
#
# if order == 'C':
# os = 1; oe = 3; oi = 0;
# else:
# os = 0; oe = 2; oi = 2;
#
# for s in sourceSlice[os:oe]:
# if s.start is not None or s.stop is not None or s.step is not None:
# raise RuntimeError('sub-regions other than in slowest dimension %d not supported! slice = %r' % (oi, sourceSlice))
#
# s = sourceSlice[oi];
# if s.step is not None:
# raise RuntimeError('sub-regions with non unity steps not supported')
#
# if s.start is None:
# s = 0;
# else:
# s = s.start;
#
# return s;
#
#
#def load(filename, region = None, shared = False, blocks = None, processes = cpu_count(), verbose = False):
# """Load a large npy array into memory in parallel
#
# Arguments:
# filename : str
# filename of array to load
# region : Region or None
# if not None this specifies the sub-region to read
# shared : bool
# if True read into shared memory
# blocks : int or None
# number of blocks to split array into for parallel processing
# processes : None or int
# number of processes, if None use number of cpus
# verbose : bool
# print info about the file to be loaded
#
# Returns:
# array
# the data as numpy array
# """
# if processes is None:
# processes = cpu_count();
# if blocks is None:
# blocks = processes * defaultBlocksPerProcess;
#
# #get specs from header specs
# shape, dtype, order, offset = readNumpyHeader(filename);
# if verbose:
# timer = tmr.Timer();
# print('Loading array of shape = %r, dtype = %r, order = %r, offset = %r' %(shape, dtype, order, offset));
#
# if region is not None:
# shape = region.shape();
# sourceSlice = region.sourceSlice();
# off = _offsetFromSlice(sourceSlice, order = order);
#
# if shared:
# data = shm.create(shape, dtype = dtype, order = order);
# else:
# data = np.empty(shape, dtype = dtype, order = order);
#
# d = data.reshape(-1, order = 'A');
# if dtype == bool:
# d = d.view('uint8');
#
# if region is not None:
# if order == 'F':
# offset += data.strides[-1] * off;
# else:
# offset += data.strides[1] * off;
#
# code.load(data = d, filename = filename, offset = offset, blocks = blocks, processes = processes);
#
# if verbose:
# timer.printElapsedTime(head = 'Loading array from %s' % filename);
#
# return data;
#
#
#
#
#def save(filename, data, region = None, blocks = None, processes = cpu_count(), verbose = False):
# """Save a large npy array to disk in parallel
#
# Arguments:
# filename : str
# filename of array to load
# data : array
# array to save to disk
# blocks : int or None
# number of blocks to split array into for parallel processing
# processes : None or int
# number of processes, if None use number of cpus
# verbose : bool
# print info about the file to be loaded
#
# Returns:
# str
# the filename of the numpy array on disk
# """
# if processes is None:
# processes = cpu_count();
# if blocks is None:
# blocks = processes * defaultBlocksPerProcess;
#
# if region is None:
# #create file on disk via memmap
# memmap = np.lib.format.open_memmap(filename, mode = 'w+', shape = data.shape, dtype = data.dtype, fortran_order = np.isfortran(data));
# memmap.flush();
# del(memmap);
#
# #get specs from header specs
# shape, dtype, order, offset = readNumpyHeader(filename);
# if verbose:
# timer = tmr.Timer();
# print('Saving array of shape = %r, dtype = %r, order = %r, offset = %r' %(shape, dtype, order, offset));
#
# if (np.isfortran(data) and order != 'F') or (not np.isfortran(data) and order != 'C'):
# raise RuntimeError('Order of arrays do not match isfortran=%r and order=%s' % (np.isfortran(data), order));
#
# d = data.reshape(-1, order = 'A');
# if dtype == bool:
# d = d.view('uint8');
#
# if region is not None:
# sourceSlice = region.sourceSlice();
# off = _offsetFromSlice(sourceSlice, order = order);
# if order == 'F':
# offset += data.strides[-1] * off;
# else:
# offset += data.strides[1] * off;
#
# #print d.dtype, filename, offset, blocks, processes
#
# code.save(data = d, filename = filename, offset = offset, blocks = blocks, processes = processes);
#
# if verbose:
# timer.printElapsedTime(head = 'Saving array to %s' % filename);
#
# return filename;
#
#
#
#
#
#
#if __name__ == "__main__":
#
# import numpy as np
# from ClearMap.Utils.Timer import Timer;
# import ClearMap.DataProcessing.LargeData as ld
# reload(ld)
#
#
# #dat = np.random.rand(2000,2000,1000) > 0.5;
# #dat = np.random.rand(1000,1000,500) > 0.5;
# dat = np.random.rand(200,300,400) > 0.5;
# #datan = io.MMP.writeData('test.npy', dat);
#
# dat = np.load('data.npy')
# xyz1 = np.load('points.npy')
#
# s = ld.sum(dat)
# print(s == np.sum(s))
#
#
# timer = Timer();
# xyz = ld.where(dat)
# timer.printElapsedTime('parallel')
# #parallel: elapsed time: 0:00:25.807
#
# timer = Timer();
# xyz1 = np.vstack(np.where(dat)).T
# timer.printElapsedTime('numpy')
# #numpy: elapsed time: 0:05:45.590
#
#
# d0 = np.zeros(dat.shape, dtype = bool);
# d1 = np.zeros(dat.shape, dtype = bool);
#
# d0[xyz[:,0], xyz[:,1], xyz[:,2]] = True;
# d1[xyz1[:,0], xyz1[:,1], xyz1[:,2]] = True;
# np.all(d0 == d1)
#
# dat2 = np.array(np.random.rand(1000, 1000, 1000) > 0, dtype = 'bool');
# filename = 'test.npy';
# np.save(filename, dat2)
#
# filename = '/disque/raid/vasculature/4X-test2/170824_IgG_2/170824_IgG_16-23-46/rank_threshold.npy'
#
# timer = Timer();
# ldat = ld.load(filename, verbose = True);
# timer.printElapsedTime('load')
# #load: elapsed time: 0:00:04.867
#
# timer = Timer();
# ldat2 = np.load(filename);
# timer.printElapsedTime('numpy')
# #numpy: elapsed time: 0:00:27.982
#
# np.all(ldat == ldat2)
#
# timer = Timer();
# xyz = ld.where(ldat)
# timer.printElapsedTime('parallel')
# #parallel: elapsed time: 0:07:25.698
#
# lldat = ldat.reshape(-1, order = 'A')
# timer = Timer();
# xyz = ld.where(lldat)
# timer.printElapsedTime('parallel 1d')
# #parallel 1d: elapsed time: 0:00:49.034
#
# timer = Timer();
# xyz = np.where(ldat)
# timer.printElapsedTime('numpy')
#
#
# import os
# #os.remove(filename)
#
# filename = './ClearMap/Test/Skeletonization/test_bin.npy';
# timer = Timer();
# ldat = ld.load(filename, shared = True, verbose = True);
# timer.printElapsedTime('load')
#
# ld.shm.isShared(ldat);
#
#
#
# import numpy as np
# from ClearMap.Utils.Timer import Timer;
# import ClearMap.DataProcessing.LargeData as ld
# reload(ld)
#
# filename = 'test_save.npy';
#
# dat = np.random.rand(100,200,100);
#
# ld.save(filename, dat)
#
#
# dat2 = ld.load(filename)
#
# np.all(dat == dat2)
#
# os.remove(filename)
#
#
#
# import numpy as np
# from ClearMap.Utils.Timer import Timer;
# import ClearMap.DataProcessing.LargeData as ld
# reload(ld)
#
# dat = np.zeros(100, dtype = bool);
# dat2 = dat.copy();
#
# indices = np.array([5,6,7,8,13,42])
#
# ld.setValue(dat, indices, True, cutoff = 0);
#
# dat2[indices] = True;
# np.all(dat2 == dat)
#
# d = ld.take(dat, indices, cutoff = 0)
# np.all(d)
#
#
# import numpy as np
# from ClearMap.Utils.Timer import Timer;
# import ClearMap.DataProcessing.LargeData as ld
# reload(ld)
#
#
# pts = np.array([0,1,5,6,10,11], dtype = int);
#
# ld.neighbours(pts, -10)
#
#
# import numpy as np
# from ClearMap.Utils.Timer import Timer;
# import ClearMap.DataProcessing.LargeData as ld
# import ClearMap.ImageProcessing.Filter.StructureElement as sel;
# reload(ld)
#
# dat = np.random.rand(30,40,50) > 0.5;
# mask = sel.structureElement('Disk', (5,5,5));
# indices = np.where(dat.reshape(-1))[0];
# c_id = len(indices)/2;
# c = indices[c_id];
# xyz = np.unravel_index(c, dat.shape)
# l = np.array(mask.shape)/2
# r = np.array(mask.shape) - l;
# dlo = [max(0,xx-ll) for xx,ll in zip(xyz,l)];
# dhi = [min(xx+rr,ss) for xx,rr,ss in zip(xyz,r, dat.shape)]
# mlo = [-min(0,xx-ll) for xx,ll in zip(xyz,l)];
# mhi = [mm + min(0, ss-xx-rr) for xx,rr,ss,mm in zip(xyz,r, dat.shape, mask.shape)]
#
# nbh = dat[dlo[0]:dhi[0], dlo[1]:dhi[1], dlo[2]:dhi[2]];
# nbhm = np.logical_and(nbh, mask[mlo[0]:mhi[0], mlo[1]:mhi[1], mlo[2]:mhi[2]] > 0);
# nxyz = np.where(nbhm);
# nxyz = [nn + dl for nn,dl in zip(nxyz, dlo)];
# nbi = np.ravel_multi_index(nxyz, dat.shape);
#
# nbs = ld.findNeighbours(indices, c_id , dat.shape, dat.strides, mask)
#
# nbs.sort();
# print np.all(nbs == nbi)
#
#
# dat = np.random.rand(30,40,50) > 0.5;
# indices = np.where(dat.reshape(-1))[0];
# c_id = len(indices)/2;
# c = indices[c_id];
# xyz = np.unravel_index(c, dat.shape)
# l = np.array([2,2,2]);
# r = l + 1;
# dlo = [max(0,xx-ll) for xx,ll in zip(xyz,l)];
# dhi = [min(xx+rr,ss) for xx,rr,ss in zip(xyz, r, dat.shape)]
# nbh = dat[dlo[0]:dhi[0], dlo[1]:dhi[1], dlo[2]:dhi[2]];
# nxyz = np.where(nbh);
# nxyz = [nn + dl for nn,dl in zip(nxyz, dlo)];
# nbi = np.ravel_multi_index(nxyz, dat.shape);
#
# nbs = ld.findNeighbours(indices, c_id , dat.shape, dat.strides, tuple(l))
#
# nbs.sort();
# print np.all(nbs == nbi)
#
# print nbs
# print nbi
#
#
# import numpy as np
# from ClearMap.Utils.Timer import Timer;
# import ClearMap.DataProcessing.LargeData as ld
# reload(ld)
#
# data = np.random.rand(100);
# values =np.random.rand(50);
# indices = np.arange(50);
# ld.setArray(data, indices, values, cutoff = 1)
# print np.all(data[:50] == values)
#
# import numpy as np
# from ClearMap.Utils.Timer import Timer;
# import ClearMap.DataProcessing.LargeData as ld
# reload(ld)
#
# m = np.array([1,3,6,7,10]);
# i = np.array([1,2,3,4,6,7,8,9]);
#
# o = ld.match(m,i)
#
# o2 = [np.where(i==l)[0][0] for l in m]
#
#
#